With the emerging trend of GPT models, we have established a framework called
AutoML-GPT that integrates a comprehensive set of tools and libraries. This
framework grants users access to a wide range of data preprocessing techniques,
feature engineering methods, and model selection algorithms. Through a
conversational interface, users can specify their requirements, constraints,
and evaluation metrics. Throughout the process, AutoML-GPT employs advanced
techniques for hyperparameter optimization and model selection, ensuring that
the resulting model achieves optimal performance. The system effectively
manages the complexity of the machine learning pipeline, guiding users towards
the best choices without requiring deep domain knowledge. Through our
experimental results on diverse datasets, we have demonstrated that AutoML-GPT
significantly reduces the time and effort required for machine learning tasks.
Its ability to leverage the vast knowledge encoded in large language models
enables it to provide valuable insights, identify potential pitfalls, and
suggest effective solutions to common challenges faced during model training.

AutoML-GPT 框架结合了多种工具和库，通过对话界面，用户可以指定需求和约束条件，实现数据预处理、特征工程和模型选择等操作，从而显著减少机器学习任务所需的时间和工作量。它能充分利用大型语言模型中的知识，为模型训练过程中的常见挑战提供宝贵见解和有效解决方案。

AutoML-GPT：AutoML 的大型语言模型

AutoML-GPT: Large Language Model for AutoML

AI tasks encompass a wide range of domains and fields. While numerous AI
models have been designed for specific tasks and applications, they often
require considerable human efforts in finding the right model architecture,
optimization algorithm, and hyperparameters. Recent advances in large language
models (LLMs) like ChatGPT show remarkable capabilities in various aspects of
reasoning, comprehension, and interaction. Consequently, we propose developing
task-oriented prompts and automatically utilizing LLMs to automate the training
pipeline. To implement this concept, we present the AutoML-GPT, which employs
GPT as the bridge to diverse AI models and dynamically trains models with
optimized hyperparameters. AutoML-GPT dynamically takes user requests from the
model and data cards and composes the corresponding prompt paragraph.
Ultimately, with this prompt paragraph, AutoML-GPT will automatically conduct
the experiments from data processing to model architecture, hyperparameter
tuning, and predicted training log. By leveraging {\ours}'s robust language
capabilities and the available AI models, AutoML-GPT can tackle numerous
intricate AI tasks across various tasks and datasets. This approach achieves
remarkable results in computer vision, natural language processing, and other
challenging areas. Extensive experiments and ablation studies demonstrate that
our method can be general, effective, and beneficial for many AI tasks.

AutoML-GPT 是一种使用大型语言模型和任务导向提示的自动化训练管道，可自动处理数据、选择最佳模型架构和超参数优化，并在计算机视觉、自然语言处理等领域实现了显著的结果，对于许多 AI 任务具有广泛的适用性。